Antimicrobial resistance is recognized as one of the greatest threats to human health worldwide [1]. Drugresistant infections take a staggering toll in the United States (US) and across the globe. Just one organism, methicillin-resistant Staphylococcus aureus (MRSA), kills more Americans every year (19,000) than emphysema, HIV/AIDS, Parkinson's disease, and homicide combined [2]. Almost 2 million Americans per year develop hospital-acquired infections (HAIs), resulting in 99,000 deaths [3], the vast majority of which are due to antibacterial (antibiotic)-resistant pathogens. Indeed, two common HAIs alone (sepsis and pneumonia) killed nearly 50,000 Americans and cost the US health care system more than $8 billion in 2006 [4]. In a recent survey, approximately half of patients in more than 1,000 intensive care units in 75 countries suffered from an infection, and infected patients had twice the risk of dying in the hospital as uninfected patients [5]. Based on studies of the costs of infections caused by antibiotic-resistant pathogens versus

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Pragmatic clinical trials are comparative effectiveness studies conducted in real-world settings to answer questions relevant to patients, clinicians, and healthcare decision makers. In contrast, explanatory clinical trials study how treatments or interventions work in carefully controlled settings and study populations, often to investigate a biological hypothesis or test a drug or device to meet regulatory requirements. The pragmatic approach requires unique methods to achieve the goal of identifying sustainable, generalizable, evidence based ways to improve healthcare. 1-5 While previous authors addressed statistical considerations, 6 little guidance is available on the methods for establishing partnerships between researchers and the people and processes in usual care settings that are necessary to conduct a pragmatic clinical trial. Based on insights from an initiative to accelerate pragmatic research, 7 this article summarizes best practices for researchers and partners in healthcare systems as they establish collaborative relationships, develop research questions, and implement sustainable pragmatic clinical trials.

Background The Centers for Medicare and Medicaid Services (CMS) use colon surgical site infection (SSI) rates to rank hospitals and apply financial penalties. The CMS’ risk-adjustment model omits potentially impactful variables that might disadvantage hospitals with complex surgical populations. Methods We analyzed adult patients who underwent colon surgery within facilities associated with HCA Healthcare from 2014 to 2016. SSIs were identified from National Health Safety Network (NHSN) reporting. We trained and validated 3 SSI prediction models, using (1) current CMS model variables, including hospital-specific random effects (HCA-adapted CMS model); (2) demographics and claims-based comorbidities (expanded-claims model); and (3) demographics, claims-based comorbidities, and NHSN variables (claims-plus–electronic health record [EHR] model). Discrimination, calibration, and resulting rankings were compared among all models and the current CMS model with published coefficient values. Results We identified 39 468 colon surgeries in 149 hospitals, resulting in 1216 (3.1%) SSIs. Compared to the HCA-adapted CMS model, the expanded-claims model had similar performance (c-statistic, 0.65 vs 0.67, respectively), while the claims-plus-EHR model was more accurate (c-statistic, 0.70; 95% confidence interval, .67–.73; P = .004). The sampling variation, due to the low surgical volume and small number of infections, contributed 74% of the total variation in observed SSI rates between hospitals. When CMS model rankings were compared to those from the expanded-claims and claims-plus-EHR models, 18 (15%) and 26 (22%) hospitals changed quartiles, respectively, and 10 (8.3%) and 12 (10%) hospitals changed into or out of the lowest-performing quartile, respectively. Conclusions An expanded set of variables improved colon SSI risk predictions and quartile assignments, but low procedure volumes and SSI events remain a barrier to effectively comparing hospitals.